Delete src

src/keras_model.h5

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-version https://git-lfs.github.com/spec/v1
-oid sha256:d80bf760a260153e1aa76937e4f82183f188a43c703abd09463e59087b1e7883
-size 2456608

src/labels.txt

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-0 PLASTICS
-1 GLASS
-2 PAPER
-3 METAL
-4 CARDBOARD

src/streamlit_app.py

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-import os
-os.environ["HOME"] = "/tmp"
-import streamlit as st
-from tensorflow.keras.models import load_model
-from tensorflow.keras.layers import DepthwiseConv2D
-from PIL import Image, ImageOps
-import numpy as np
-
-# Optional: Patch DepthwiseConv2D if needed
-class PatchedDepthwiseConv2D(DepthwiseConv2D):
-    def __init__(self, *args, groups=1, **kwargs):
-        super().__init__(*args, **kwargs)
-
-# Load model
-model = load_model(r"src/keras_model.h5", compile=False, custom_objects={"DepthwiseConv2D": PatchedDepthwiseConv2D})
-
-# Load class labels
-with open(r"src/labels.txt", "r") as f:
-    class_names = f.readlines()
-
-st.title("♻️ Garbage Classification Predictor")
-
-# Upload image
-uploaded_file = st.file_uploader("Upload a waste image (jpg, png)", type=["jpg", "jpeg", "png"])
-
-if st.button("🧪 Predict Waste Type"):
-    if uploaded_file is not None:
-        image = Image.open(uploaded_file)
-        st.image(image, use_container_width=True)
-
-
-        # Preprocess image
-        image = image.convert("RGB")
-        image = ImageOps.fit(image, (224, 224), Image.Resampling.LANCZOS)
-        image_array = np.asarray(image)
-        normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
-        data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
-        data[0] = normalized_image_array
-
-        # Make prediction
-        prediction = model.predict(data)
-        index = np.argmax(prediction)
-        predicted_label = class_names[index].strip()
-        confidence = prediction[0][index]
-
-        # Display result
-        st.success(f"Predicted Waste Type: **{predicted_label.upper()}**")
-        st.write(f"Confidence Score: **{confidence:.2f}**")
-        st.write("♻️ Dispose responsibly!")
-    else:
-        st.warning("⚠️ Please upload an image before predicting.")
-# 🔚 Footer
-st.markdown("---")
-st.markdown("<p style='text-align: center; font-size: 18px;'>Developed for EDUNET FOUNDATION </p>", unsafe_allow_html=True)
-